The proposed method achieves good clustering performance on popular computer vision datasets.
Similar to all the algorithms considered, we assume that “\(K\)  - the number of clusters" are known. However, this may not hold true in practice in real world applications. In such a case, we may assume an estimate of upper bound on the number of clusters to use as the number of prototypes. Additionally, we also assume that the dataset is equally distributed among the “\(K\) ” clusters. If this assumption (also common in literature [1]}, [2]}) does not hold, the fast Sinkhorn-Knopp algorithm used to solve Eq. REF  may not be optimal.
